In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.io as pio
pio.renderers.default = "notebook_connected"
df = pd.read_csv('cleaned_india_housing_prices.csv')
In [2]:
df.head()
Out[2]:
id state city locality property_type bhk size_in_sqft price_in_lakhs price_per_sqft year_built ... age_of_property nearby_schools nearby_hospitals public_transport_accessibility parking_space security amenities facing owner_type availability_status
0 1 Tamil Nadu Chennai Locality_84 Apartment 1 4740 489.76 10332.489451 1990 ... 35 10 3 High No No Playground, Gym, Garden, Pool, Clubhouse West Owner Ready_to_Move
1 2 Maharashtra Pune Locality_490 Independent House 3 2364 195.52 8270.727580 2008 ... 17 8 1 Low No Yes Playground, Clubhouse, Pool, Gym, Garden North Builder Under_Construction
2 3 Punjab Ludhiana Locality_167 Apartment 2 3642 183.79 5046.403075 1997 ... 28 9 8 Low Yes No Clubhouse, Pool, Playground, Gym South Broker Ready_to_Move
3 4 Rajasthan Jodhpur Locality_393 Independent House 2 2741 300.29 10955.490697 1991 ... 34 5 7 High Yes Yes Playground, Clubhouse, Gym, Pool, Garden North Builder Ready_to_Move
4 5 Rajasthan Jaipur Locality_466 Villa 4 4823 182.90 3792.245490 2002 ... 23 4 9 Low No Yes Playground, Garden, Gym, Pool, Clubhouse East Builder Ready_to_Move

5 rows × 23 columns

In [3]:
plt.figure(figsize=(8,5))
sns.histplot(df['price_in_lakhs'], bins=50, kde=True)
plt.title('Distribution of Property Prices (in Lakhs)')
plt.xlabel('Price (Lakhs)')
Out[3]:
Text(0.5, 0, 'Price (Lakhs)')
No description has been provided for this image
In [4]:
plt.figure(figsize=(8,5))
sns.histplot(df['size_in_sqft'], bins=50, kde=True)
plt.title('Distribution of Property Size (sqft)')
Out[4]:
Text(0.5, 1.0, 'Distribution of Property Size (sqft)')
No description has been provided for this image
In [5]:
plt.figure(figsize=(10,6))
sns.boxplot(x='property_type', y='price_per_sqft', data=df)
plt.xticks(rotation=45)
Out[5]:
([0, 1, 2],
 [Text(0, 0, 'Apartment'),
  Text(1, 0, 'Independent House'),
  Text(2, 0, 'Villa')])
No description has been provided for this image
In [6]:
fig = px.scatter(
    df,
    x='size_in_sqft',
    y='price_in_lakhs',
    color='city',
    hover_data=['locality', 'property_type']
)
plt.tight_layout()
fig.show()

<Figure size 640x480 with 0 Axes>
In [7]:
state_avg = df.groupby('state')['price_per_sqft'].median().sort_values(ascending=False).reset_index()
plt.tight_layout()
px.bar(state_avg, x='state', y='price_per_sqft', title='Median Price per Sqft by State')

<Figure size 640x480 with 0 Axes>
In [8]:
city_avg = df.groupby('city')['price_in_lakhs'].median().sort_values(ascending=False).head(20).reset_index()
plt.tight_layout()
px.bar(city_avg, x='city', y='price_in_lakhs', title='Top 20 Cities by Median Price')

<Figure size 640x480 with 0 Axes>
In [9]:
ct = pd.crosstab(df['city'], df['bhk'])
ct.iloc[:10,:].plot(kind='bar', stacked=True, figsize=(12,6))
plt.ylabel("Number of Properties")
plt.title("BHK Distribution Across Cities (Top 10)")
plt.tight_layout()
plt.show()
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In [10]:
df['parking_space'] = df['parking_space'].str.strip().str.lower().map({'yes':1, 'no':0})

num_cols = ['price_in_lakhs','size_in_sqft','price_per_sqft','age_of_property','nearby_schools','nearby_hospitals','parking_space']
plt.figure(figsize=(10,8))
sns.heatmap(df[num_cols].corr(), annot=True, fmt='.2f', cmap='coolwarm')
Out[10]:
<Axes: >
No description has been provided for this image
In [11]:
px.box(df, x='furnished_status', y='price_per_sqft', title='Price per Sqft by Furnished Status')
In [12]:
count_data = df['owner_type'].value_counts().reset_index()
count_data.columns = ['owner_type', 'count']

fig = px.bar(
    count_data,
    x='owner_type',
    y='count',
    color='owner_type',   
    title='Distribution of Owner Types',
    labels={'count': 'Number of Properties', 'owner_type': 'Owner Type'},
    hover_data=['count']
)

fig.show()
In [13]:
avail_data = df['availability_status'].value_counts().reset_index()
avail_data.columns = ['availability_status', 'count']

fig = px.bar(
    avail_data,
    x='availability_status',
    y='count',
    color='availability_status',   
    title='Distribution of Availability Status',
    labels={'count': 'Number of Properties', 'availability_status': 'Availability Status'},
    hover_data=['count']
)

fig.show()
In [14]:
# convert amenities text to count
df['amenities_count'] = df['amenities'].apply(lambda x: len(str(x).split(',')) if pd.notna(x) else 0)
px.scatter(df, x='amenities_count', y='price_per_sqft', trendline='ols')
In [ ]: